Trust-based fusion of classifiers for static code analysis

Name: Trust-based fusion of classifiers for static code analysis
Author: Yüksel, U., Sözer, Hasan, Şensoy, Murat

İsim	Trust-based fusion of classifiers for static code analysis
Yazar	Yüksel, U., Sözer, Hasan, Şensoy, Murat
Basım Tarihi:	2014
Basım Yeri	- IEEE
Konu	Classifer fusion, Trust-based fusion, Alert classification, Industrial case study, Static code analysis
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-84910594174
Kayıt Numarası	da8fa851-6ea7-4a78-bccf-c998a9b71cd2
Lokasyon	Computer Science
Tarih	2014
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Static code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. The effectiveness of many different classifiers and artifact characteristics have been evaluated for this application domain. However, the effectiveness of classifier fusion methods have not been investigated yet. In this work, we evaluate several existing classifier fusion approaches in the context of an industrial case study to classify the alerts generated for a digital TV software. In addition, we employ a trust-based classifier fusion method. We observed that our approach can increase the accuracy of classification by up to 4%.

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Trust-based fusion of classifiers for static code analysis

Yazar Yüksel, U., Sözer, Hasan, Şensoy, Murat

Basım Tarihi 2014

Basım Yeri - IEEE

Konu Classifer fusion, Trust-based fusion, Alert classification, Industrial case study, Static code analysis

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-84910594174

Kayıt Numarası da8fa851-6ea7-4a78-bccf-c998a9b71cd2

Lokasyon Computer Science

Tarih 2014

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Static code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. The effectiveness of many different classifiers and artifact characteristics have been evaluated for this application domain. However, the effectiveness of classifier fusion methods have not been investigated yet. In this work, we evaluate several existing classifier fusion approaches in the context of an industrial case study to classify the alerts generated for a digital TV software. In addition, we employ a trust-based classifier fusion method. We observed that our approach can increase the accuracy of classification by up to 4%.